Overview

Dataset statistics

Number of variables34
Number of observations119210
Missing cells0
Missing cells (%)0.0%
Duplicate rows8180
Duplicate rows (%)6.9%
Total size in memory30.9 MiB
Average record size in memory272.0 B

Variable types

Categorical15
Numeric16
Text1
DateTime2

Alerts

Dataset has 8180 (6.9%) duplicate rowsDuplicates
agent is highly overall correlated with hotelHigh correlation
assigned_room_type is highly overall correlated with reserved_room_typeHigh correlation
distribution_channel is highly overall correlated with market_segmentHigh correlation
hotel is highly overall correlated with agentHigh correlation
is_canceled is highly overall correlated with reservation_status and 1 other fieldsHigh correlation
is_repeated_guest is highly overall correlated with previous_bookings_not_canceled and 1 other fieldsHigh correlation
market_segment is highly overall correlated with distribution_channelHigh correlation
previous_bookings_not_canceled is highly overall correlated with is_repeated_guest and 1 other fieldsHigh correlation
previous_cancellations is highly overall correlated with total_bookingsHigh correlation
reservation_status is highly overall correlated with is_canceled and 1 other fieldsHigh correlation
reserved_room_type is highly overall correlated with assigned_room_typeHigh correlation
stay_duration is highly overall correlated with is_canceled and 1 other fieldsHigh correlation
total_bookings is highly overall correlated with is_repeated_guest and 2 other fieldsHigh correlation
babies is highly imbalanced (97.1%)Imbalance
meal is highly imbalanced (53.5%)Imbalance
distribution_channel is highly imbalanced (63.2%)Imbalance
is_repeated_guest is highly imbalanced (79.8%)Imbalance
reserved_room_type is highly imbalanced (56.3%)Imbalance
deposit_type is highly imbalanced (65.3%)Imbalance
customer_type is highly imbalanced (50.6%)Imbalance
required_car_parking_spaces is highly imbalanced (85.4%)Imbalance
previous_cancellations is highly skewed (γ1 = 24.44392359)Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.53955539)Skewed
lead_time has 6264 (5.3%) zerosZeros
stays_in_weekend_nights has 51895 (43.5%) zerosZeros
stays_in_week_nights has 7572 (6.4%) zerosZeros
children has 109878 (92.2%) zerosZeros
previous_cancellations has 112731 (94.6%) zerosZeros
previous_bookings_not_canceled has 115597 (97.0%) zerosZeros
booking_changes has 101232 (84.9%) zerosZeros
days_in_waiting_list has 115517 (96.9%) zerosZeros
adr has 1810 (1.5%) zerosZeros
total_of_special_requests has 70201 (58.9%) zerosZeros
total_bookings has 109762 (92.1%) zerosZeros
stay_duration has 2717 (2.3%) zerosZeros

Reproduction

Analysis started2024-02-25 19:02:29.509962
Analysis finished2024-02-25 19:04:34.438398
Duration2 minutes and 4.93 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

hotel
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
City Hotel
79163 
Resort Hotel
40047 

Length

Max length12
Median length10
Mean length10.671873
Min length10

Characters and Unicode

Total characters1272194
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResort Hotel
2nd rowResort Hotel
3rd rowResort Hotel
4th rowResort Hotel
5th rowResort Hotel

Common Values

ValueCountFrequency (%)
City Hotel 79163
66.4%
Resort Hotel 40047
33.6%

Length

2024-02-26T00:34:34.747319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:35.018783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
hotel 119210
50.0%
city 79163
33.2%
resort 40047
 
16.8%

Most occurring characters

ValueCountFrequency (%)
t 238420
18.7%
o 159257
12.5%
e 159257
12.5%
119210
9.4%
H 119210
9.4%
l 119210
9.4%
C 79163
 
6.2%
i 79163
 
6.2%
y 79163
 
6.2%
R 40047
 
3.1%
Other values (2) 80094
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 914564
71.9%
Uppercase Letter 238420
 
18.7%
Space Separator 119210
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 238420
26.1%
o 159257
17.4%
e 159257
17.4%
l 119210
13.0%
i 79163
 
8.7%
y 79163
 
8.7%
s 40047
 
4.4%
r 40047
 
4.4%
Uppercase Letter
ValueCountFrequency (%)
H 119210
50.0%
C 79163
33.2%
R 40047
 
16.8%
Space Separator
ValueCountFrequency (%)
119210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1152984
90.6%
Common 119210
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 238420
20.7%
o 159257
13.8%
e 159257
13.8%
H 119210
10.3%
l 119210
10.3%
C 79163
 
6.9%
i 79163
 
6.9%
y 79163
 
6.9%
R 40047
 
3.5%
s 40047
 
3.5%
Common
ValueCountFrequency (%)
119210
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1272194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 238420
18.7%
o 159257
12.5%
e 159257
12.5%
119210
9.4%
H 119210
9.4%
l 119210
9.4%
C 79163
 
6.2%
i 79163
 
6.2%
y 79163
 
6.2%
R 40047
 
3.1%
Other values (2) 80094
 
6.3%

is_canceled
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
75011 
1
44199 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 75011
62.9%
1 44199
37.1%

Length

2024-02-26T00:34:35.193473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:35.397531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 75011
62.9%
1 44199
37.1%

Most occurring characters

ValueCountFrequency (%)
0 75011
62.9%
1 44199
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 75011
62.9%
1 44199
37.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 75011
62.9%
1 44199
37.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 75011
62.9%
1 44199
37.1%

lead_time
Real number (ℝ)

ZEROS 

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.10923
Minimum0
Maximum737
Zeros6264
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:35.593719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3161
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)143

Descriptive statistics

Standard deviation106.87545
Coefficient of variation (CV)1.0265704
Kurtosis1.6943723
Mean104.10923
Median Absolute Deviation (MAD)60
Skewness1.3458092
Sum12410861
Variance11422.362
MonotonicityNot monotonic
2024-02-26T00:34:35.799401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6264
 
5.3%
1 3445
 
2.9%
2 2065
 
1.7%
3 1815
 
1.5%
4 1710
 
1.4%
5 1563
 
1.3%
6 1444
 
1.2%
7 1329
 
1.1%
8 1138
 
1.0%
12 1079
 
0.9%
Other values (469) 97358
81.7%
ValueCountFrequency (%)
0 6264
5.3%
1 3445
2.9%
2 2065
 
1.7%
3 1815
 
1.5%
4 1710
 
1.4%
5 1563
 
1.3%
6 1444
 
1.2%
7 1329
 
1.1%
8 1138
 
1.0%
9 991
 
0.8%
ValueCountFrequency (%)
737 1
 
< 0.1%
709 1
 
< 0.1%
629 17
< 0.1%
626 30
< 0.1%
622 17
< 0.1%
615 17
< 0.1%
608 17
< 0.1%
605 30
< 0.1%
601 17
< 0.1%
594 17
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2016
56623 
2017
40620 
2015
21967 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters476840
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2016 56623
47.5%
2017 40620
34.1%
2015 21967
 
18.4%

Length

2024-02-26T00:34:36.010113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:36.187084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2016 56623
47.5%
2017 40620
34.1%
2015 21967
 
18.4%

Most occurring characters

ValueCountFrequency (%)
2 119210
25.0%
0 119210
25.0%
1 119210
25.0%
6 56623
11.9%
7 40620
 
8.5%
5 21967
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 476840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 119210
25.0%
0 119210
25.0%
1 119210
25.0%
6 56623
11.9%
7 40620
 
8.5%
5 21967
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 476840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 119210
25.0%
0 119210
25.0%
1 119210
25.0%
6 56623
11.9%
7 40620
 
8.5%
5 21967
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 476840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 119210
25.0%
0 119210
25.0%
1 119210
25.0%
6 56623
11.9%
7 40620
 
8.5%
5 21967
 
4.6%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
August
13861 
July
12644 
May
11780 
October
11147 
April
11078 
Other values (7)
58700 

Length

Max length9
Median length7
Mean length5.9026927
Min length3

Characters and Unicode

Total characters703660
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJuly
2nd rowJuly
3rd rowJuly
4th rowJuly
5th rowJuly

Common Values

ValueCountFrequency (%)
August 13861
11.6%
July 12644
10.6%
May 11780
9.9%
October 11147
9.4%
April 11078
9.3%
June 10929
9.2%
September 10500
8.8%
March 9768
8.2%
February 8052
6.8%
November 6771
5.7%
Other values (2) 12680
10.6%

Length

2024-02-26T00:34:36.408310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 13861
11.6%
july 12644
10.6%
may 11780
9.9%
october 11147
9.4%
april 11078
9.3%
june 10929
9.2%
september 10500
8.8%
march 9768
8.2%
february 8052
6.8%
november 6771
5.7%
Other values (2) 12680
10.6%

Most occurring characters

ValueCountFrequency (%)
e 95447
13.6%
r 78048
 
11.1%
u 65268
 
9.3%
b 43229
 
6.1%
a 41442
 
5.9%
y 38397
 
5.5%
t 35508
 
5.0%
J 29494
 
4.2%
c 27674
 
3.9%
A 24939
 
3.5%
Other values (16) 224214
31.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 584450
83.1%
Uppercase Letter 119210
 
16.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 95447
16.3%
r 78048
13.4%
u 65268
11.2%
b 43229
 
7.4%
a 41442
 
7.1%
y 38397
 
6.6%
t 35508
 
6.1%
c 27674
 
4.7%
m 24030
 
4.1%
l 23722
 
4.1%
Other values (8) 111685
19.1%
Uppercase Letter
ValueCountFrequency (%)
J 29494
24.7%
A 24939
20.9%
M 21548
18.1%
O 11147
 
9.4%
S 10500
 
8.8%
F 8052
 
6.8%
N 6771
 
5.7%
D 6759
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 703660
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 95447
13.6%
r 78048
 
11.1%
u 65268
 
9.3%
b 43229
 
6.1%
a 41442
 
5.9%
y 38397
 
5.5%
t 35508
 
5.0%
J 29494
 
4.2%
c 27674
 
3.9%
A 24939
 
3.5%
Other values (16) 224214
31.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 703660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 95447
13.6%
r 78048
 
11.1%
u 65268
 
9.3%
b 43229
 
6.1%
a 41442
 
5.9%
y 38397
 
5.5%
t 35508
 
5.0%
J 29494
 
4.2%
c 27674
 
3.9%
A 24939
 
3.5%
Other values (16) 224214
31.9%

arrival_date_week_number
Real number (ℝ)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.163376
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:36.703993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.601107
Coefficient of variation (CV)0.5007149
Kurtosis-0.98542287
Mean27.163376
Median Absolute Deviation (MAD)11
Skewness-0.010198696
Sum3238146
Variance184.99011
MonotonicityNot monotonic
2024-02-26T00:34:37.017316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 3576
 
3.0%
30 3082
 
2.6%
32 3041
 
2.6%
34 3039
 
2.5%
18 2923
 
2.5%
21 2853
 
2.4%
28 2843
 
2.4%
17 2803
 
2.4%
20 2781
 
2.3%
29 2763
 
2.3%
Other values (43) 89506
75.1%
ValueCountFrequency (%)
1 1045
0.9%
2 1216
1.0%
3 1318
1.1%
4 1485
1.2%
5 1385
1.2%
6 1507
1.3%
7 2102
1.8%
8 2212
1.9%
9 2109
1.8%
10 2142
1.8%
ValueCountFrequency (%)
53 1811
1.5%
52 1187
1.0%
51 933
0.8%
50 1498
1.3%
49 1780
1.5%
48 1495
1.3%
47 1677
1.4%
46 1570
1.3%
45 1940
1.6%
44 2270
1.9%

arrival_date_day_of_month
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.798717
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:37.280577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7810701
Coefficient of variation (CV)0.55580908
Kurtosis-1.1870963
Mean15.798717
Median Absolute Deviation (MAD)8
Skewness-0.0021109856
Sum1883365
Variance77.107192
MonotonicityNot monotonic
2024-02-26T00:34:37.492034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17 4401
 
3.7%
5 4310
 
3.6%
15 4188
 
3.5%
25 4155
 
3.5%
26 4141
 
3.5%
9 4090
 
3.4%
12 4082
 
3.4%
16 4071
 
3.4%
2 4054
 
3.4%
19 4048
 
3.4%
Other values (21) 77670
65.2%
ValueCountFrequency (%)
1 3620
3.0%
2 4054
3.4%
3 3847
3.2%
4 3760
3.2%
5 4310
3.6%
6 3819
3.2%
7 3658
3.1%
8 3919
3.3%
9 4090
3.4%
10 3569
3.0%
ValueCountFrequency (%)
31 2207
1.9%
30 3844
3.2%
29 3580
3.0%
28 3942
3.3%
27 3791
3.2%
26 4141
3.5%
25 4155
3.5%
24 3983
3.3%
23 3612
3.0%
22 3593
3.0%

stays_in_weekend_nights
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9270531
Minimum0
Maximum19
Zeros51895
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:37.683327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.99511703
Coefficient of variation (CV)1.0734197
Kurtosis6.3653972
Mean0.9270531
Median Absolute Deviation (MAD)1
Skewness1.3202425
Sum110514
Variance0.9902579
MonotonicityNot monotonic
2024-02-26T00:34:37.844558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 51895
43.5%
2 33266
27.9%
1 30615
25.7%
4 1847
 
1.5%
3 1252
 
1.1%
6 152
 
0.1%
5 77
 
0.1%
8 58
 
< 0.1%
7 19
 
< 0.1%
9 10
 
< 0.1%
Other values (7) 19
 
< 0.1%
ValueCountFrequency (%)
0 51895
43.5%
1 30615
25.7%
2 33266
27.9%
3 1252
 
1.1%
4 1847
 
1.5%
5 77
 
0.1%
6 152
 
0.1%
7 19
 
< 0.1%
8 58
 
< 0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
16 2
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 5
 
< 0.1%
10 7
 
< 0.1%
9 10
 
< 0.1%
8 58
< 0.1%
7 19
 
< 0.1%

stays_in_week_nights
Real number (ℝ)

ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4991947
Minimum0
Maximum50
Zeros7572
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:38.021216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8971058
Coefficient of variation (CV)0.75908683
Kurtosis22.250866
Mean2.4991947
Median Absolute Deviation (MAD)1
Skewness2.7548629
Sum297929
Variance3.5990103
MonotonicityNot monotonic
2024-02-26T00:34:38.257438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 33670
28.2%
1 30292
25.4%
3 22241
18.7%
5 11068
 
9.3%
4 9543
 
8.0%
0 7572
 
6.4%
6 1494
 
1.3%
10 1030
 
0.9%
7 1024
 
0.9%
8 654
 
0.5%
Other values (23) 622
 
0.5%
ValueCountFrequency (%)
0 7572
 
6.4%
1 30292
25.4%
2 33670
28.2%
3 22241
18.7%
4 9543
 
8.0%
5 11068
 
9.3%
6 1494
 
1.3%
7 1024
 
0.9%
8 654
 
0.5%
9 228
 
0.2%
ValueCountFrequency (%)
50 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 4
< 0.1%
26 1
 
< 0.1%
25 6
< 0.1%
24 3
< 0.1%

adults
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8592064
Minimum0
Maximum55
Zeros223
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:38.404088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57518558
Coefficient of variation (CV)0.30937155
Kurtosis1392.5063
Mean1.8592064
Median Absolute Deviation (MAD)0
Skewness18.774333
Sum221636
Variance0.33083845
MonotonicityNot monotonic
2024-02-26T00:34:38.526418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 89680
75.2%
1 23027
 
19.3%
3 6202
 
5.2%
0 223
 
0.2%
4 62
 
0.1%
26 5
 
< 0.1%
27 2
 
< 0.1%
20 2
 
< 0.1%
5 2
 
< 0.1%
40 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 223
 
0.2%
1 23027
 
19.3%
2 89680
75.2%
3 6202
 
5.2%
4 62
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
10 1
 
< 0.1%
20 2
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
55 1
 
< 0.1%
50 1
 
< 0.1%
40 1
 
< 0.1%
27 2
 
< 0.1%
26 5
 
< 0.1%
20 2
 
< 0.1%
10 1
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 62
0.1%

children
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11200403
Minimum0
Maximum10
Zeros109878
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2024-02-26T00:34:38.676561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41284929
Coefficient of variation (CV)3.6860218
Kurtosis21.375007
Mean0.11200403
Median Absolute Deviation (MAD)0
Skewness4.0953519
Sum13352
Variance0.17044453
MonotonicityNot monotonic
2024-02-26T00:34:38.812553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 109878
92.2%
1 5446
 
4.6%
2 3772
 
3.2%
3 111
 
0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 109878
92.2%
1 5446
 
4.6%
2 3772
 
3.2%
3 111
 
0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 1
 
< 0.1%
3 111
 
0.1%
2 3772
 
3.2%
1 5446
 
4.6%
0 109878
92.2%

babies
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
118293 
1
 
900
2
 
15
10
 
1
9
 
1

Length

Max length2
Median length1
Mean length1.0000084
Min length1

Characters and Unicode

Total characters119211
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 118293
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Length

2024-02-26T00:34:38.949245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:39.193465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 118293
99.2%
1 900
 
0.8%
2 15
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 118294
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119211
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118294
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119211
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118294
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118294
99.2%
1 901
 
0.8%
2 15
 
< 0.1%
9 1
 
< 0.1%

meal
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
BB
92236 
HB
14458 
SC
10549 
Undefined
 
1169
FB
 
798

Length

Max length9
Median length2
Mean length2.0686436
Min length2

Characters and Unicode

Total characters246603
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBB
2nd rowBB
3rd rowBB
4th rowBB
5th rowBB

Common Values

ValueCountFrequency (%)
BB 92236
77.4%
HB 14458
 
12.1%
SC 10549
 
8.8%
Undefined 1169
 
1.0%
FB 798
 
0.7%

Length

2024-02-26T00:34:39.354535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:39.538381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bb 92236
77.4%
hb 14458
 
12.1%
sc 10549
 
8.8%
undefined 1169
 
1.0%
fb 798
 
0.7%

Most occurring characters

ValueCountFrequency (%)
B 199728
81.0%
H 14458
 
5.9%
S 10549
 
4.3%
C 10549
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 237251
96.2%
Lowercase Letter 9352
 
3.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 199728
84.2%
H 14458
 
6.1%
S 10549
 
4.4%
C 10549
 
4.4%
U 1169
 
0.5%
F 798
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
n 2338
25.0%
d 2338
25.0%
e 2338
25.0%
f 1169
12.5%
i 1169
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 246603
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 199728
81.0%
H 14458
 
5.9%
S 10549
 
4.3%
C 10549
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 246603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 199728
81.0%
H 14458
 
5.9%
S 10549
 
4.3%
C 10549
 
4.3%
n 2338
 
0.9%
d 2338
 
0.9%
e 2338
 
0.9%
U 1169
 
0.5%
f 1169
 
0.5%
i 1169
 
0.5%
Distinct177
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:39.972952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.989271
Min length2

Characters and Unicode

Total characters356351
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowGBR
4th rowGBR
5th rowGBR
ValueCountFrequency (%)
prt 48961
41.1%
gbr 12120
 
10.2%
fra 10401
 
8.7%
esp 8560
 
7.2%
deu 7285
 
6.1%
ita 3761
 
3.2%
irl 3374
 
2.8%
bel 2342
 
2.0%
bra 2222
 
1.9%
nld 2103
 
1.8%
Other values (167) 18081
 
15.2%
2024-02-26T00:34:40.472505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 81146
22.8%
P 58867
16.5%
T 54629
15.3%
A 21602
 
6.1%
E 21520
 
6.0%
B 17040
 
4.8%
S 13911
 
3.9%
U 13284
 
3.7%
G 13120
 
3.7%
F 10941
 
3.1%
Other values (16) 50291
14.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 356351
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 81146
22.8%
P 58867
16.5%
T 54629
15.3%
A 21602
 
6.1%
E 21520
 
6.0%
B 17040
 
4.8%
S 13911
 
3.9%
U 13284
 
3.7%
G 13120
 
3.7%
F 10941
 
3.1%
Other values (16) 50291
14.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 356351
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 81146
22.8%
P 58867
16.5%
T 54629
15.3%
A 21602
 
6.1%
E 21520
 
6.0%
B 17040
 
4.8%
S 13911
 
3.9%
U 13284
 
3.7%
G 13120
 
3.7%
F 10941
 
3.1%
Other values (16) 50291
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 356351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 81146
22.8%
P 58867
16.5%
T 54629
15.3%
A 21602
 
6.1%
E 21520
 
6.0%
B 17040
 
4.8%
S 13911
 
3.9%
U 13284
 
3.7%
G 13120
 
3.7%
F 10941
 
3.1%
Other values (16) 50291
14.1%

market_segment
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Online TA
56408 
Offline TA/TO
24182 
Groups
19791 
Direct
12582 
Corporate
 
5282
Other values (3)
 
965

Length

Max length13
Median length9
Mean length9.0191762
Min length6

Characters and Unicode

Total characters1075176
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowOnline TA

Common Values

ValueCountFrequency (%)
Online TA 56408
47.3%
Offline TA/TO 24182
20.3%
Groups 19791
 
16.6%
Direct 12582
 
10.6%
Corporate 5282
 
4.4%
Complementary 728
 
0.6%
Aviation 235
 
0.2%
Undefined 2
 
< 0.1%

Length

2024-02-26T00:34:40.647312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:40.835136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
online 56408
28.2%
ta 56408
28.2%
offline 24182
12.1%
ta/to 24182
12.1%
groups 19791
 
9.9%
direct 12582
 
6.3%
corporate 5282
 
2.6%
complementary 728
 
0.4%
aviation 235
 
0.1%
undefined 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 137965
12.8%
O 104772
9.7%
T 104772
9.7%
e 99914
9.3%
i 93644
8.7%
l 81318
7.6%
A 80825
7.5%
80590
7.5%
f 48366
 
4.5%
r 43665
 
4.1%
Other values (16) 199345
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 641650
59.7%
Uppercase Letter 328754
30.6%
Space Separator 80590
 
7.5%
Other Punctuation 24182
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 137965
21.5%
e 99914
15.6%
i 93644
14.6%
l 81318
12.7%
f 48366
 
7.5%
r 43665
 
6.8%
o 31318
 
4.9%
p 25801
 
4.0%
s 19791
 
3.1%
u 19791
 
3.1%
Other values (7) 40077
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
O 104772
31.9%
T 104772
31.9%
A 80825
24.6%
G 19791
 
6.0%
D 12582
 
3.8%
C 6010
 
1.8%
U 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
80590
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 24182
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 970404
90.3%
Common 104772
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 137965
14.2%
O 104772
10.8%
T 104772
10.8%
e 99914
10.3%
i 93644
9.7%
l 81318
8.4%
A 80825
8.3%
f 48366
 
5.0%
r 43665
 
4.5%
o 31318
 
3.2%
Other values (14) 143845
14.8%
Common
ValueCountFrequency (%)
80590
76.9%
/ 24182
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1075176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 137965
12.8%
O 104772
9.7%
T 104772
9.7%
e 99914
9.3%
i 93644
8.7%
l 81318
7.6%
A 80825
7.5%
80590
7.5%
f 48366
 
4.5%
r 43665
 
4.1%
Other values (16) 199345
18.5%

distribution_channel
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
TA/TO
97750 
Direct
14611 
Corporate
 
6651
GDS
 
193
Undefined
 
5

Length

Max length9
Median length5
Mean length5.3426642
Min length3

Characters and Unicode

Total characters636899
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect
2nd rowDirect
3rd rowDirect
4th rowCorporate
5th rowTA/TO

Common Values

ValueCountFrequency (%)
TA/TO 97750
82.0%
Direct 14611
 
12.3%
Corporate 6651
 
5.6%
GDS 193
 
0.2%
Undefined 5
 
< 0.1%

Length

2024-02-26T00:34:41.043263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:41.221317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta/to 97750
82.0%
direct 14611
 
12.3%
corporate 6651
 
5.6%
gds 193
 
0.2%
undefined 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 195500
30.7%
/ 97750
15.3%
O 97750
15.3%
A 97750
15.3%
r 27913
 
4.4%
e 21272
 
3.3%
t 21262
 
3.3%
D 14804
 
2.3%
i 14616
 
2.3%
c 14611
 
2.3%
Other values (10) 33671
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 412846
64.8%
Lowercase Letter 126303
 
19.8%
Other Punctuation 97750
 
15.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 27913
22.1%
e 21272
16.8%
t 21262
16.8%
i 14616
11.6%
c 14611
11.6%
o 13302
10.5%
a 6651
 
5.3%
p 6651
 
5.3%
n 10
 
< 0.1%
d 10
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 195500
47.4%
O 97750
23.7%
A 97750
23.7%
D 14804
 
3.6%
C 6651
 
1.6%
G 193
 
< 0.1%
S 193
 
< 0.1%
U 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 97750
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 539149
84.7%
Common 97750
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 195500
36.3%
O 97750
18.1%
A 97750
18.1%
r 27913
 
5.2%
e 21272
 
3.9%
t 21262
 
3.9%
D 14804
 
2.7%
i 14616
 
2.7%
c 14611
 
2.7%
o 13302
 
2.5%
Other values (9) 20369
 
3.8%
Common
ValueCountFrequency (%)
/ 97750
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 636899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 195500
30.7%
/ 97750
15.3%
O 97750
15.3%
A 97750
15.3%
r 27913
 
4.4%
e 21272
 
3.3%
t 21262
 
3.3%
D 14804
 
2.3%
i 14616
 
2.3%
c 14611
 
2.3%
Other values (10) 33671
 
5.3%

is_repeated_guest
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
115455 
1
 
3755

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 115455
96.9%
1 3755
 
3.1%

Length

2024-02-26T00:34:41.363877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:41.513449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 115455
96.9%
1 3755
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 115455
96.9%
1 3755
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 115455
96.9%
1 3755
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 115455
96.9%
1 3755
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 115455
96.9%
1 3755
 
3.1%

previous_cancellations
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087190672
Minimum0
Maximum26
Zeros112731
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:41.647368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84491826
Coefficient of variation (CV)9.6904663
Kurtosis673.22115
Mean0.087190672
Median Absolute Deviation (MAD)0
Skewness24.443924
Sum10394
Variance0.71388687
MonotonicityNot monotonic
2024-02-26T00:34:41.801262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 112731
94.6%
1 6048
 
5.1%
2 114
 
0.1%
3 65
 
0.1%
24 48
 
< 0.1%
11 35
 
< 0.1%
4 31
 
< 0.1%
26 26
 
< 0.1%
25 25
 
< 0.1%
6 22
 
< 0.1%
Other values (5) 65
 
0.1%
ValueCountFrequency (%)
0 112731
94.6%
1 6048
 
5.1%
2 114
 
0.1%
3 65
 
0.1%
4 31
 
< 0.1%
5 19
 
< 0.1%
6 22
 
< 0.1%
11 35
 
< 0.1%
13 12
 
< 0.1%
14 14
 
< 0.1%
ValueCountFrequency (%)
26 26
< 0.1%
25 25
< 0.1%
24 48
< 0.1%
21 1
 
< 0.1%
19 19
 
< 0.1%
14 14
 
< 0.1%
13 12
 
< 0.1%
11 35
< 0.1%
6 22
< 0.1%
5 19
 
< 0.1%

previous_bookings_not_canceled
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1370942
Minimum0
Maximum72
Zeros115597
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:41.967529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4981372
Coefficient of variation (CV)10.927794
Kurtosis766.95283
Mean0.1370942
Median Absolute Deviation (MAD)0
Skewness23.539555
Sum16343
Variance2.244415
MonotonicityNot monotonic
2024-02-26T00:34:42.139320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115597
97.0%
1 1538
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 113
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 59
 
< 0.1%
Other values (63) 422
 
0.4%
ValueCountFrequency (%)
0 115597
97.0%
1 1538
 
1.3%
2 580
 
0.5%
3 333
 
0.3%
4 229
 
0.2%
5 181
 
0.2%
6 113
 
0.1%
7 88
 
0.1%
8 70
 
0.1%
9 59
 
< 0.1%
ValueCountFrequency (%)
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
68 1
< 0.1%
67 1
< 0.1%
66 1
< 0.1%
65 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%

reserved_room_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
A
85873 
D
19179 
E
 
6519
F
 
2894
G
 
2092
Other values (4)
 
2653

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 85873
72.0%
D 19179
 
16.1%
E 6519
 
5.5%
F 2894
 
2.4%
G 2092
 
1.8%
B 1115
 
0.9%
C 931
 
0.8%
H 601
 
0.5%
L 6
 
< 0.1%

Length

2024-02-26T00:34:42.294372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:42.446115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 85873
72.0%
d 19179
 
16.1%
e 6519
 
5.5%
f 2894
 
2.4%
g 2092
 
1.8%
b 1115
 
0.9%
c 931
 
0.8%
h 601
 
0.5%
l 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 85873
72.0%
D 19179
 
16.1%
E 6519
 
5.5%
F 2894
 
2.4%
G 2092
 
1.8%
B 1115
 
0.9%
C 931
 
0.8%
H 601
 
0.5%
L 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 119210
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 85873
72.0%
D 19179
 
16.1%
E 6519
 
5.5%
F 2894
 
2.4%
G 2092
 
1.8%
B 1115
 
0.9%
C 931
 
0.8%
H 601
 
0.5%
L 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 119210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 85873
72.0%
D 19179
 
16.1%
E 6519
 
5.5%
F 2894
 
2.4%
G 2092
 
1.8%
B 1115
 
0.9%
C 931
 
0.8%
H 601
 
0.5%
L 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 85873
72.0%
D 19179
 
16.1%
E 6519
 
5.5%
F 2894
 
2.4%
G 2092
 
1.8%
B 1115
 
0.9%
C 931
 
0.8%
H 601
 
0.5%
L 6
 
< 0.1%

assigned_room_type
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
A
74020 
D
25309 
E
7798 
F
 
3751
G
 
2549
Other values (6)
 
5783

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 74020
62.1%
D 25309
 
21.2%
E 7798
 
6.5%
F 3751
 
3.1%
G 2549
 
2.1%
C 2370
 
2.0%
B 2154
 
1.8%
H 712
 
0.6%
I 359
 
0.3%
K 187
 
0.2%

Length

2024-02-26T00:34:42.591595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 74020
62.1%
d 25309
 
21.2%
e 7798
 
6.5%
f 3751
 
3.1%
g 2549
 
2.1%
c 2370
 
2.0%
b 2154
 
1.8%
h 712
 
0.6%
i 359
 
0.3%
k 187
 
0.2%

Most occurring characters

ValueCountFrequency (%)
A 74020
62.1%
D 25309
 
21.2%
E 7798
 
6.5%
F 3751
 
3.1%
G 2549
 
2.1%
C 2370
 
2.0%
B 2154
 
1.8%
H 712
 
0.6%
I 359
 
0.3%
K 187
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 119210
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 74020
62.1%
D 25309
 
21.2%
E 7798
 
6.5%
F 3751
 
3.1%
G 2549
 
2.1%
C 2370
 
2.0%
B 2154
 
1.8%
H 712
 
0.6%
I 359
 
0.3%
K 187
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 119210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 74020
62.1%
D 25309
 
21.2%
E 7798
 
6.5%
F 3751
 
3.1%
G 2549
 
2.1%
C 2370
 
2.0%
B 2154
 
1.8%
H 712
 
0.6%
I 359
 
0.3%
K 187
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 74020
62.1%
D 25309
 
21.2%
E 7798
 
6.5%
F 3751
 
3.1%
G 2549
 
2.1%
C 2370
 
2.0%
B 2154
 
1.8%
H 712
 
0.6%
I 359
 
0.3%
K 187
 
0.2%

booking_changes
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21879876
Minimum0
Maximum18
Zeros101232
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:42.717292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63850446
Coefficient of variation (CV)2.9182271
Kurtosis63.437992
Mean0.21879876
Median Absolute Deviation (MAD)0
Skewness5.5000578
Sum26083
Variance0.40768794
MonotonicityNot monotonic
2024-02-26T00:34:42.861308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 101232
84.9%
1 12666
 
10.6%
2 3780
 
3.2%
3 914
 
0.8%
4 367
 
0.3%
5 115
 
0.1%
6 61
 
0.1%
7 29
 
< 0.1%
8 14
 
< 0.1%
9 8
 
< 0.1%
Other values (9) 24
 
< 0.1%
ValueCountFrequency (%)
0 101232
84.9%
1 12666
 
10.6%
2 3780
 
3.2%
3 914
 
0.8%
4 367
 
0.3%
5 115
 
0.1%
6 61
 
0.1%
7 29
 
< 0.1%
8 14
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 3
 
< 0.1%
14 3
 
< 0.1%
13 5
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 6
< 0.1%
9 8
< 0.1%

deposit_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
No Deposit
104461 
Non Refund
14587 
Refundable
 
162

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1192100
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Deposit
2nd rowNo Deposit
3rd rowNo Deposit
4th rowNo Deposit
5th rowNo Deposit

Common Values

ValueCountFrequency (%)
No Deposit 104461
87.6%
Non Refund 14587
 
12.2%
Refundable 162
 
0.1%

Length

2024-02-26T00:34:43.010422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:43.165967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 104461
43.8%
deposit 104461
43.8%
non 14587
 
6.1%
refund 14587
 
6.1%
refundable 162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 223509
18.7%
e 119372
10.0%
N 119048
10.0%
119048
10.0%
s 104461
8.8%
i 104461
8.8%
t 104461
8.8%
p 104461
8.8%
D 104461
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 834794
70.0%
Uppercase Letter 238258
 
20.0%
Space Separator 119048
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 223509
26.8%
e 119372
14.3%
s 104461
12.5%
i 104461
12.5%
t 104461
12.5%
p 104461
12.5%
n 29336
 
3.5%
f 14749
 
1.8%
u 14749
 
1.8%
d 14749
 
1.8%
Other values (3) 486
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 119048
50.0%
D 104461
43.8%
R 14749
 
6.2%
Space Separator
ValueCountFrequency (%)
119048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1073052
90.0%
Common 119048
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 223509
20.8%
e 119372
11.1%
N 119048
11.1%
s 104461
9.7%
i 104461
9.7%
t 104461
9.7%
p 104461
9.7%
D 104461
9.7%
n 29336
 
2.7%
R 14749
 
1.4%
Other values (6) 44733
 
4.2%
Common
ValueCountFrequency (%)
119048
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1192100
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 223509
18.7%
e 119372
10.0%
N 119048
10.0%
119048
10.0%
s 104461
8.8%
i 104461
8.8%
t 104461
8.8%
p 104461
8.8%
D 104461
8.8%
n 29336
 
2.5%
Other values (7) 59482
 
5.0%

agent
Real number (ℝ)

HIGH CORRELATION 

Distinct333
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.11817
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:43.301265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median9
Q3152
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)143

Descriptive statistics

Standard deviation106.35144
Coefficient of variation (CV)1.3971886
Kurtosis0.54174967
Mean76.11817
Median Absolute Deviation (MAD)5
Skewness1.3092548
Sum9074047
Variance11310.628
MonotonicityNot monotonic
2024-02-26T00:34:43.462400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 48202
40.4%
240 13922
 
11.7%
1 7187
 
6.0%
14 3633
 
3.0%
7 3532
 
3.0%
6 3290
 
2.8%
250 2870
 
2.4%
241 1721
 
1.4%
28 1657
 
1.4%
8 1514
 
1.3%
Other values (323) 31682
26.6%
ValueCountFrequency (%)
1 7187
 
6.0%
2 162
 
0.1%
3 1336
 
1.1%
4 47
 
< 0.1%
5 330
 
0.3%
6 3290
 
2.8%
7 3532
 
3.0%
8 1514
 
1.3%
9 48202
40.4%
10 260
 
0.2%
ValueCountFrequency (%)
535 3
 
< 0.1%
531 68
0.1%
527 35
< 0.1%
526 10
 
< 0.1%
510 2
 
< 0.1%
509 10
 
< 0.1%
508 6
 
< 0.1%
502 24
 
< 0.1%
497 1
 
< 0.1%
495 57
< 0.1%

days_in_waiting_list
Real number (ℝ)

ZEROS 

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3212147
Minimum0
Maximum391
Zeros115517
Zeros (%)96.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:43.630499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.598002
Coefficient of variation (CV)7.5813763
Kurtosis186.89459
Mean2.3212147
Median Absolute Deviation (MAD)0
Skewness11.948868
Sum276712
Variance309.68967
MonotonicityNot monotonic
2024-02-26T00:34:43.795264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115517
96.9%
39 227
 
0.2%
58 164
 
0.1%
44 141
 
0.1%
31 127
 
0.1%
35 96
 
0.1%
46 94
 
0.1%
69 89
 
0.1%
63 83
 
0.1%
87 80
 
0.1%
Other values (117) 2592
 
2.2%
ValueCountFrequency (%)
0 115517
96.9%
1 12
 
< 0.1%
2 5
 
< 0.1%
3 59
 
< 0.1%
4 25
 
< 0.1%
5 8
 
< 0.1%
6 15
 
< 0.1%
7 4
 
< 0.1%
8 7
 
< 0.1%
9 16
 
< 0.1%
ValueCountFrequency (%)
391 45
< 0.1%
379 15
 
< 0.1%
330 15
 
< 0.1%
259 10
 
< 0.1%
236 35
< 0.1%
224 10
 
< 0.1%
223 61
0.1%
215 21
 
< 0.1%
207 15
 
< 0.1%
193 1
 
< 0.1%

customer_type
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Transient
89476 
Transient-Party
25088 
Contract
 
4072
Group
 
574

Length

Max length15
Median length9
Mean length10.209295
Min length5

Characters and Unicode

Total characters1217050
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTransient
2nd rowTransient
3rd rowTransient
4th rowTransient
5th rowTransient

Common Values

ValueCountFrequency (%)
Transient 89476
75.1%
Transient-Party 25088
 
21.0%
Contract 4072
 
3.4%
Group 574
 
0.5%

Length

2024-02-26T00:34:43.971309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:44.117731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
transient 89476
75.1%
transient-party 25088
 
21.0%
contract 4072
 
3.4%
group 574
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 233200
19.2%
t 147796
12.1%
r 144298
11.9%
a 143724
11.8%
T 114564
9.4%
s 114564
9.4%
i 114564
9.4%
e 114564
9.4%
y 25088
 
2.1%
- 25088
 
2.1%
Other values (7) 39600
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1047664
86.1%
Uppercase Letter 144298
 
11.9%
Dash Punctuation 25088
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 233200
22.3%
t 147796
14.1%
r 144298
13.8%
a 143724
13.7%
s 114564
10.9%
i 114564
10.9%
e 114564
10.9%
y 25088
 
2.4%
o 4646
 
0.4%
c 4072
 
0.4%
Other values (2) 1148
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
T 114564
79.4%
P 25088
 
17.4%
C 4072
 
2.8%
G 574
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 25088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1191962
97.9%
Common 25088
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 233200
19.6%
t 147796
12.4%
r 144298
12.1%
a 143724
12.1%
T 114564
9.6%
s 114564
9.6%
i 114564
9.6%
e 114564
9.6%
y 25088
 
2.1%
P 25088
 
2.1%
Other values (6) 14512
 
1.2%
Common
ValueCountFrequency (%)
- 25088
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1217050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 233200
19.2%
t 147796
12.1%
r 144298
11.9%
a 143724
11.8%
T 114564
9.4%
s 114564
9.4%
i 114564
9.4%
e 114564
9.4%
y 25088
 
2.1%
- 25088
 
2.1%
Other values (7) 39600
 
3.3%

adr
Real number (ℝ)

ZEROS 

Distinct8866
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.96909
Minimum-6.38
Maximum5400
Zeros1810
Zeros (%)1.5%
Negative1
Negative (%)< 0.1%
Memory size1.8 MiB
2024-02-26T00:34:44.672457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile39
Q169.5
median94.95
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation50.434007
Coefficient of variation (CV)0.49460092
Kurtosis1022.8267
Mean101.96909
Median Absolute Deviation (MAD)27.95
Skewness10.612728
Sum12155735
Variance2543.589
MonotonicityNot monotonic
2024-02-26T00:34:44.838122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 3754
 
3.1%
75 2715
 
2.3%
90 2472
 
2.1%
65 2418
 
2.0%
80 1889
 
1.6%
0 1810
 
1.5%
95 1661
 
1.4%
120 1607
 
1.3%
100 1573
 
1.3%
85 1538
 
1.3%
Other values (8856) 97773
82.0%
ValueCountFrequency (%)
-6.38 1
 
< 0.1%
0 1810
1.5%
0.26 1
 
< 0.1%
0.5 1
 
< 0.1%
1 14
 
< 0.1%
1.48 1
 
< 0.1%
1.56 2
 
< 0.1%
1.6 1
 
< 0.1%
1.8 1
 
< 0.1%
2 12
 
< 0.1%
ValueCountFrequency (%)
5400 1
< 0.1%
510 1
< 0.1%
508 1
< 0.1%
451.5 1
< 0.1%
450 1
< 0.1%
437 1
< 0.1%
426.25 1
< 0.1%
402 1
< 0.1%
397.38 1
< 0.1%
392 2
< 0.1%

required_car_parking_spaces
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
0
111801 
1
 
7376
2
 
28
3
 
3
8
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters119210
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 111801
93.8%
1 7376
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Length

2024-02-26T00:34:45.094919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:45.314676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 111801
93.8%
1 7376
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 111801
93.8%
1 7376
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119210
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111801
93.8%
1 7376
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 119210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111801
93.8%
1 7376
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111801
93.8%
1 7376
 
6.2%
2 28
 
< 0.1%
3 3
 
< 0.1%
8 2
 
< 0.1%

total_of_special_requests
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57150407
Minimum0
Maximum5
Zeros70201
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:45.474779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7928759
Coefficient of variation (CV)1.3873495
Kurtosis1.4926142
Mean0.57150407
Median Absolute Deviation (MAD)0
Skewness1.3490487
Sum68129
Variance0.62865219
MonotonicityNot monotonic
2024-02-26T00:34:45.687752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 70201
58.9%
1 33183
27.8%
2 12952
 
10.9%
3 2494
 
2.1%
4 340
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
0 70201
58.9%
1 33183
27.8%
2 12952
 
10.9%
3 2494
 
2.1%
4 340
 
0.3%
5 40
 
< 0.1%
ValueCountFrequency (%)
5 40
 
< 0.1%
4 340
 
0.3%
3 2494
 
2.1%
2 12952
 
10.9%
1 33183
27.8%
0 70201
58.9%

reservation_status
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Check-Out
75011 
Canceled
42993 
No-Show
 
1206

Length

Max length9
Median length9
Mean length8.6191175
Min length7

Characters and Unicode

Total characters1027485
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCheck-Out
2nd rowCheck-Out
3rd rowCheck-Out
4th rowCheck-Out
5th rowCheck-Out

Common Values

ValueCountFrequency (%)
Check-Out 75011
62.9%
Canceled 42993
36.1%
No-Show 1206
 
1.0%

Length

2024-02-26T00:34:45.956140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T00:34:46.149360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
check-out 75011
62.9%
canceled 42993
36.1%
no-show 1206
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 160997
15.7%
C 118004
11.5%
c 118004
11.5%
h 76217
7.4%
- 76217
7.4%
u 75011
7.3%
t 75011
7.3%
O 75011
7.3%
k 75011
7.3%
a 42993
 
4.2%
Other values (7) 135009
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 755841
73.6%
Uppercase Letter 195427
 
19.0%
Dash Punctuation 76217
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 160997
21.3%
c 118004
15.6%
h 76217
10.1%
u 75011
9.9%
t 75011
9.9%
k 75011
9.9%
a 42993
 
5.7%
n 42993
 
5.7%
l 42993
 
5.7%
d 42993
 
5.7%
Other values (2) 3618
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 118004
60.4%
O 75011
38.4%
N 1206
 
0.6%
S 1206
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 76217
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 951268
92.6%
Common 76217
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 160997
16.9%
C 118004
12.4%
c 118004
12.4%
h 76217
8.0%
u 75011
7.9%
t 75011
7.9%
O 75011
7.9%
k 75011
7.9%
a 42993
 
4.5%
n 42993
 
4.5%
Other values (6) 92016
9.7%
Common
ValueCountFrequency (%)
- 76217
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1027485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 160997
15.7%
C 118004
11.5%
c 118004
11.5%
h 76217
7.4%
- 76217
7.4%
u 75011
7.3%
t 75011
7.3%
O 75011
7.3%
k 75011
7.3%
a 42993
 
4.2%
Other values (7) 135009
13.1%
Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2014-10-17 00:00:00
Maximum2017-09-14 00:00:00
2024-02-26T00:34:46.317332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:46.507283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct793
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Minimum2015-07-01 00:00:00
Maximum2017-08-31 00:00:00
2024-02-26T00:34:46.667336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:46.844671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_bookings
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22428488
Minimum0
Maximum78
Zeros109762
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2024-02-26T00:34:47.043347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum78
Range78
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8288107
Coefficient of variation (CV)8.1539636
Kurtosis521.5149
Mean0.22428488
Median Absolute Deviation (MAD)0
Skewness19.276343
Sum26737
Variance3.3445486
MonotonicityNot monotonic
2024-02-26T00:34:47.216476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109762
92.1%
1 7119
 
6.0%
2 609
 
0.5%
3 336
 
0.3%
4 235
 
0.2%
5 170
 
0.1%
6 130
 
0.1%
7 91
 
0.1%
8 77
 
0.1%
9 65
 
0.1%
Other values (67) 616
 
0.5%
ValueCountFrequency (%)
0 109762
92.1%
1 7119
 
6.0%
2 609
 
0.5%
3 336
 
0.3%
4 235
 
0.2%
5 170
 
0.1%
6 130
 
0.1%
7 91
 
0.1%
8 77
 
0.1%
9 65
 
0.1%
ValueCountFrequency (%)
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
75 1
< 0.1%
74 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%

stay_duration
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7780975
Minimum-1
Maximum69
Zeros2717
Zeros (%)2.3%
Negative42129
Negative (%)35.3%
Memory size1.4 MiB
2024-02-26T00:34:47.387596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q33
95-th percentile7
Maximum69
Range70
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9161384
Coefficient of variation (CV)1.6400329
Kurtosis12.134098
Mean1.7780975
Median Absolute Deviation (MAD)2
Skewness1.8582684
Sum211967
Variance8.5038631
MonotonicityNot monotonic
2024-02-26T00:34:47.529445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-1 42129
35.3%
1 15732
 
13.2%
3 15716
 
13.2%
2 15470
 
13.0%
4 11014
 
9.2%
7 5672
 
4.8%
5 5107
 
4.3%
0 2717
 
2.3%
6 2316
 
1.9%
10 776
 
0.7%
Other values (30) 2561
 
2.1%
ValueCountFrequency (%)
-1 42129
35.3%
0 2717
 
2.3%
1 15732
 
13.2%
2 15470
 
13.0%
3 15716
 
13.2%
4 11014
 
9.2%
5 5107
 
4.3%
6 2316
 
1.9%
7 5672
 
4.8%
8 713
 
0.6%
ValueCountFrequency (%)
69 1
 
< 0.1%
60 1
 
< 0.1%
56 1
 
< 0.1%
48 1
 
< 0.1%
46 1
 
< 0.1%
45 1
 
< 0.1%
42 3
< 0.1%
38 1
 
< 0.1%
35 5
< 0.1%
34 1
 
< 0.1%

Interactions

2024-02-26T00:34:28.125522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:46.566206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:49.301907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:51.763354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:54.576193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:57.080670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:59.532206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:02.285345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:04.665053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:07.102954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:09.716229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:12.483365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:15.439283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:18.645154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:21.492558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:24.762351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:28.278784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:46.815438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:49.477674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:51.996055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:54.736281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:57.241174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:59.792346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:02.438347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:04.835662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:07.353231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:09.866090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:12.624398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:15.751436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:18.807188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:21.792391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:24.948089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:28.415360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:46.980260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:49.631682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:52.189372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:54.885144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:57.388553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:59.951993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:02.597274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:04.976352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:07.507849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:10.013864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:12.759856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:16.002368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:18.985120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:22.011496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:25.107976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:28.579503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:47.279193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:49.780176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:52.364770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:55.039181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:57.533206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:00.125123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:02.746624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:05.120224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:07.666393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:10.204658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:12.892900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:16.292404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:19.143986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:22.243418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:25.299416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:28.749433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:47.431128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:49.934712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:52.544242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:55.188748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:57.681910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:00.290674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:02.892196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:05.272397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:07.845494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:17.154126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:08.600321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:11.397333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:13.820109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:17.486185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:23.485492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:33:56.121325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:58.570788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:33:56.263902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:58.705394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:01.358267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:03.880567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:06.344332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:08.879417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:11.676165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:14.125157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:17.842389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:20.405811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:27.219804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:30.523161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:20.620487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:27.400634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:30.896049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:20.799387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:24.234712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:27.579429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:06.792197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:18.282443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:34:27.752664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-02-26T00:33:56.911549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:33:59.369079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:02.123979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:04.499933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:06.946336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:09.560471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:12.313510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:15.154010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:18.462198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:21.198304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:24.596657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-02-26T00:34:27.960076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-02-26T00:34:47.715723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
adradultsagentarrival_date_day_of_montharrival_date_montharrival_date_week_numberarrival_date_yearassigned_room_typebabiesbooking_changeschildrencustomer_typedays_in_waiting_listdeposit_typedistribution_channelhotelis_canceledis_repeated_guestlead_timemarket_segmentmealprevious_bookings_not_canceledprevious_cancellationsrequired_car_parking_spacesreservation_statusreserved_room_typestay_durationstays_in_week_nightsstays_in_weekend_nightstotal_bookingstotal_of_special_requests
adr1.0000.276-0.0050.0270.0010.0740.0000.0000.0000.0080.2600.000-0.0400.0070.0000.0000.0000.0000.0130.0000.000-0.143-0.1500.0000.0000.000-0.0100.0930.051-0.2000.197
adults0.2761.0000.0030.0010.0100.0270.0150.0000.000-0.0810.0760.090-0.0370.0000.0080.0140.0130.0000.1900.0080.000-0.210-0.0370.0000.0080.0040.0330.1530.127-0.1390.162
agent-0.0050.0031.0000.0040.076-0.0450.0840.1150.0220.0590.0530.1120.0000.1050.1580.6860.0600.049-0.0540.1870.163-0.035-0.1540.0870.0460.1210.1380.1800.145-0.1420.035
arrival_date_day_of_month0.0270.0010.0041.0000.0580.0610.0440.0090.0050.0120.0150.0320.0320.0540.0280.0250.0210.0180.0080.0330.039-0.002-0.0120.0080.0230.0100.002-0.016-0.007-0.0110.003
arrival_date_month0.0010.0100.0760.0581.0000.3370.4290.0280.016-0.016-0.0900.1030.0390.1010.0690.0700.0690.0730.0570.0880.089-0.0080.0460.0180.0650.048-0.008-0.024-0.0350.034-0.057
arrival_date_week_number0.0740.027-0.0450.0610.3371.0000.4240.0270.0140.0080.0100.106-0.0040.0950.0640.0680.0650.0740.1130.0810.080-0.0440.0870.0170.0610.0440.0090.0260.0260.0480.019
arrival_date_year0.0000.0150.0840.0440.4290.4241.0000.0530.0090.0230.0520.213-0.0830.0520.0270.0430.0260.0090.0570.1590.1120.037-0.2580.0180.0230.082-0.0080.0420.025-0.1940.116
assigned_room_type0.0000.0000.1150.0090.0280.0270.0531.0000.0440.0960.2610.090-0.0990.1920.0950.3910.2010.060-0.1580.1180.1130.041-0.1390.0920.1440.7440.1810.0830.080-0.0890.137
babies0.0000.0000.0220.0050.0160.0140.0090.0441.0000.1170.2970.015-0.0140.0230.0290.0490.0340.007-0.0200.0340.015-0.011-0.0170.0200.0240.0400.0420.0260.023-0.0200.093
booking_changes0.008-0.0810.0590.012-0.0160.0080.0230.0960.1171.0000.0940.038-0.0190.0570.0410.0490.0790.001-0.0070.0290.0120.031-0.0730.0260.0570.0180.1930.0630.039-0.0430.042
children0.2600.0760.0530.015-0.0900.0100.0520.2610.2970.0941.0000.041-0.0500.0480.0310.0450.0160.023-0.0210.0740.026-0.037-0.0610.0220.0180.3370.0280.0590.058-0.0690.117
customer_type0.0000.0900.1120.0320.1030.1060.2130.0900.0150.0380.0411.0000.1060.0980.0800.0520.1370.1060.1250.2750.139-0.0490.0020.0410.0970.1090.049-0.080-0.083-0.026-0.160
days_in_waiting_list-0.040-0.0370.0000.0320.039-0.004-0.083-0.099-0.014-0.019-0.0500.1061.0000.1280.0270.0870.0680.0240.1530.0780.062-0.0190.1160.0340.0500.028-0.0870.012-0.0750.083-0.123
deposit_type0.0070.0000.1050.0540.1010.0950.0520.1920.0230.0570.0480.0980.1281.0000.0910.1770.4820.0580.3310.3740.093-0.0640.3180.0710.3470.152-0.421-0.056-0.1160.229-0.302
distribution_channel0.0000.0080.1580.0280.0690.0640.0270.0950.0290.0410.0310.0800.0270.0911.0000.1870.1770.3000.2960.6920.078-0.2900.0070.0760.1290.100-0.0780.1210.099-0.1430.109
hotel0.0000.0140.6860.0250.0700.0680.0430.3910.0490.0490.0450.0520.0870.1770.1871.0000.1370.052-0.0860.1470.3170.084-0.0840.2210.1370.3230.1780.1810.166-0.0140.046
is_canceled0.0000.0130.0600.0210.0690.0650.0260.2010.0340.0790.0160.1370.0680.4820.1770.1371.0000.0840.3160.2670.050-0.1150.2700.1981.0000.072-0.8590.041-0.0040.155-0.259
is_repeated_guest0.0000.0000.0490.0180.0730.0740.0090.0600.0070.0010.0230.1060.0240.0580.3000.0520.0841.000-0.1880.3490.0620.7620.1540.0790.0850.037-0.008-0.126-0.0960.5310.005
lead_time0.0130.190-0.0540.0080.0570.1130.057-0.158-0.020-0.007-0.0210.1250.1530.3310.296-0.0860.316-0.1881.0000.1700.089-0.1890.1710.0570.2070.051-0.1190.2960.1620.038-0.074
market_segment0.0000.0080.1870.0330.0880.0810.1590.1180.0340.0290.0740.2750.0780.3740.6920.1470.2670.3490.1701.0000.192-0.211-0.1660.0930.1960.1350.0280.1260.115-0.2450.374
meal0.0000.0000.1630.0390.0890.0800.1120.1130.0150.0120.0260.1390.0620.0930.0780.3170.0500.0620.0890.1921.000-0.066-0.0400.0270.0400.1020.0290.0310.051-0.0680.029
previous_bookings_not_canceled-0.143-0.210-0.035-0.002-0.008-0.0440.0370.041-0.0110.031-0.037-0.049-0.019-0.064-0.2900.084-0.1150.762-0.189-0.211-0.0661.0000.1010.0190.0290.0040.021-0.119-0.0840.6200.025
previous_cancellations-0.150-0.037-0.154-0.0120.0460.087-0.258-0.139-0.017-0.073-0.0610.0020.1160.3180.007-0.0840.2700.1540.171-0.166-0.0400.1011.0000.0000.0310.007-0.249-0.062-0.0550.809-0.130
required_car_parking_spaces0.0000.0000.0870.0080.0180.0170.0180.0920.0200.0260.0220.0410.0340.0710.0760.2210.1980.0790.0570.0930.0270.0190.0001.0000.1400.0790.149-0.034-0.0190.0230.088
reservation_status0.0000.0080.0460.0230.0650.0610.0230.1440.0240.0570.0180.0970.0500.3470.1290.1371.0000.0850.2070.1960.0400.0290.0310.1401.0000.0520.831-0.0460.007-0.1590.254
reserved_room_type0.0000.0040.1210.0100.0480.0440.0820.7440.0400.0180.3370.1090.0280.1520.1000.3230.0720.0370.0510.1350.1020.0040.0070.0790.0521.0000.1270.1700.142-0.1090.152
stay_duration-0.0100.0330.1380.002-0.0080.009-0.0080.1810.0420.1930.0280.049-0.087-0.421-0.0780.178-0.859-0.008-0.1190.0280.0290.021-0.2490.1490.8310.1271.0000.3240.267-0.1900.264
stays_in_week_nights0.0930.1530.180-0.016-0.0240.0260.0420.0830.0260.0630.059-0.0800.012-0.0560.1210.1810.041-0.1260.2960.1260.031-0.119-0.062-0.034-0.0460.1700.3241.0000.237-0.1150.076
stays_in_weekend_nights0.0510.1270.145-0.007-0.0350.0260.0250.0800.0230.0390.058-0.083-0.075-0.1160.0990.166-0.004-0.0960.1620.1150.051-0.084-0.055-0.0190.0070.1420.2670.2371.000-0.0900.080
total_bookings-0.200-0.139-0.142-0.0110.0340.048-0.194-0.089-0.020-0.043-0.069-0.0260.0830.229-0.143-0.0140.1550.5310.038-0.245-0.0680.6200.8090.023-0.159-0.109-0.190-0.115-0.0901.000-0.096
total_of_special_requests0.1970.1620.0350.003-0.0570.0190.1160.1370.0930.0420.117-0.160-0.123-0.3020.1090.046-0.2590.005-0.0740.3740.0290.025-0.1300.0880.2540.1520.2640.0760.080-0.0961.000

Missing values

2024-02-26T00:34:32.380341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T00:34:33.495195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datearrival_datetotal_bookingsstay_duration
0Resort Hotel03422015July27100200BBPRTDirectDirect000CC3No Deposit9.00Transient0.000Check-Out2015-07-012015-07-0100
1Resort Hotel07372015July27100200BBPRTDirectDirect000CC4No Deposit9.00Transient0.000Check-Out2015-07-012015-07-0100
2Resort Hotel072015July27101100BBGBRDirectDirect000AC0No Deposit9.00Transient75.000Check-Out2015-07-022015-07-0101
3Resort Hotel0132015July27101100BBGBRCorporateCorporate000AA0No Deposit304.00Transient75.000Check-Out2015-07-022015-07-0101
4Resort Hotel0142015July27102200BBGBROnline TATA/TO000AA0No Deposit240.00Transient98.001Check-Out2015-07-032015-07-0102
5Resort Hotel0142015July27102200BBGBROnline TATA/TO000AA0No Deposit240.00Transient98.001Check-Out2015-07-032015-07-0102
6Resort Hotel002015July27102200BBPRTDirectDirect000CC0No Deposit9.00Transient107.000Check-Out2015-07-032015-07-0102
7Resort Hotel092015July27102200FBPRTDirectDirect000CC0No Deposit303.00Transient103.001Check-Out2015-07-032015-07-0102
8Resort Hotel1852015July27103200BBPRTOnline TATA/TO000AA0No Deposit240.00Transient82.001Canceled2015-05-062015-07-010-1
9Resort Hotel1752015July27103200HBPRTOffline TA/TOTA/TO000DD0No Deposit15.00Transient105.500Canceled2015-04-222015-07-010-1
hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datearrival_datetotal_bookingsstay_duration
119380City Hotel0442017August353113200SCDEUOnline TATA/TO000AA0No Deposit9.00Transient140.7501Check-Out2017-09-042017-08-3104
119381City Hotel01882017August353123200BBDEUDirectDirect000AA0No Deposit14.00Transient99.0000Check-Out2017-09-052017-08-3105
119382City Hotel01352017August353024300BBJPNOnline TATA/TO000GG0No Deposit7.00Transient209.0000Check-Out2017-09-052017-08-3006
119383City Hotel01642017August353124200BBDEUOffline TA/TOTA/TO000AA0No Deposit42.00Transient87.6000Check-Out2017-09-062017-08-3106
119384City Hotel0212017August353025200BBBELOffline TA/TOTA/TO000AA0No Deposit394.00Transient96.1402Check-Out2017-09-062017-08-3007
119385City Hotel0232017August353025200BBBELOffline TA/TOTA/TO000AA0No Deposit394.00Transient96.1400Check-Out2017-09-062017-08-3007
119386City Hotel01022017August353125300BBFRAOnline TATA/TO000EE0No Deposit9.00Transient225.4302Check-Out2017-09-072017-08-3107
119387City Hotel0342017August353125200BBDEUOnline TATA/TO000DD0No Deposit9.00Transient157.7104Check-Out2017-09-072017-08-3107
119388City Hotel01092017August353125200BBGBROnline TATA/TO000AA0No Deposit89.00Transient104.4000Check-Out2017-09-072017-08-3107
119389City Hotel02052017August352927200HBDEUOnline TATA/TO000AA0No Deposit9.00Transient151.2002Check-Out2017-09-072017-08-2909

Duplicate rows

Most frequently occurring

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentdays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datearrival_datetotal_bookingsstay_duration# duplicates
5400City Hotel12772016November46712200BBPRTGroupsTA/TO000AA0Non Refund9.00Transient100.000Canceled2016-04-042016-11-070-1180
4178City Hotel1682016February81702200BBPRTGroupsTA/TO010AA0Non Refund37.00Transient75.000Canceled2016-01-062016-02-171-1150
5072City Hotel11882016June251502100BBPRTOffline TA/TOTA/TO000AA0Non Refund119.039Transient130.000Canceled2016-01-182016-06-150-1109
4876City Hotel11582016May222402100BBPRTGroupsTA/TO000AA0Non Refund37.031Transient130.000Canceled2016-01-182016-05-240-1101
3847City Hotel1342015December50802100BBPRTOffline TA/TOTA/TO010AA0Non Refund19.00Transient90.000Canceled2015-11-172015-12-081-1100
3789City Hotel1282017March9203200BBPRTGroupsTA/TO000AA0Non Refund9.00Transient95.000Canceled2017-02-022017-03-020-199
3903City Hotel1382017January21401100BBPRTCorporateCorporate000AA0Non Refund9.00Transient75.000Canceled2016-12-072017-01-140-199
4869City Hotel11562017April172603200BBPRTGroupsTA/TO000AA0Non Refund37.00Transient100.000Canceled2016-11-212017-04-260-199
4202City Hotel1712016June251403100BBPRTOffline TA/TOTA/TO000AA0Non Refund236.00Transient120.000Canceled2016-04-272016-06-140-189
4936City Hotel11662016November45103100BBPRTOffline TA/TOTA/TO000AA0Non Refund236.00Transient110.000Canceled2016-07-132016-11-010-185